{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T02:38:28Z","timestamp":1770431908295,"version":"3.49.0"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2007,1,31]],"date-time":"2007-01-31T00:00:00Z","timestamp":1170201600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genetic Epidemiology"],"published-print":{"date-parts":[[2007,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The success of genetic dissection of complex diseases may greatly benefit from judicious exploration of joint gene effects, which, in turn, critically depends on the power of statistical tools. Standard regression models are convenient for assessing main effects and low\u2010order gene\u2013gene interactions but not for exploring complex higher\u2010order interactions. Tree\u2010based methodology is an attractive alternative for disentangling possible interactions, but it has difficulty in modeling additive main effects. This work proposes a new class of semiparametric regression models, termed partially linear tree\u2010based regression (PLTR) models, which exhibit the advantages of both generalized linear regression and tree models. A PLTR model quantifies joint effects of genes and other risk factors by a combination of linear main effects and a non\u2010parametric tree \u2010structure. We propose an iterative algorithm to fit the PLTR model, and a unified resampling approach for identifying and testing the significance of the optimal \u201cpruned\u201d tree nested within the tree resultant from the fitting algorithm. Simulation studies showed that the resampling procedure maintained the correct type I error rate. We applied the PLTR model to assess the association between biliary stone risk and 53 single nucleotide polymorphisms (SNPs) in the inflammation pathway in a population\u2010based case\u2010control study. The analysis yielded an interesting parsimonious summary of the joint effect of all SNPs. The proposed model is also useful for exploring gene\u2013environment interactions and has broad implications for applying the tree methodology to genetic epidemiology research. <jats:italic>Genet. Epidemiol<\/jats:italic>. \u00a9 2007 Wiley\u2010Liss, Inc.<\/jats:p>","DOI":"10.1002\/gepi.20205","type":"journal-article","created":{"date-parts":[[2007,2,3]],"date-time":"2007-02-03T04:44:16Z","timestamp":1170477856000},"page":"238-251","source":"Crossref","is-referenced-by-count":22,"title":["A partially linear tree\u2010based regression model for assessing complex joint gene\u2013gene and gene\u2013environment effects"],"prefix":"10.1002","volume":"31","author":[{"given":"Jinbo","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ann","family":"Hsing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Terry M.","family":"Therneau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2007,1,31]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1159\/000083029"},{"key":"e_1_2_1_3_1","volume-title":"Classification and Regression Trees","author":"Breiman L","year":"1984"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1159\/000073729"},{"key":"e_1_2_1_5_1","first-page":"377","volume-title":"Tree\u2010Based Models. 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